arXiv:2610.34220v1 Announce Type: new Abstract: Speech-based Alzheimer's Disease (AD) detection holds significant promise due to its scalable and non-invasive nature. However, current Speech Large Language Models (SpeechLLMs), similar to conventional AD detection systems, still treat AD detection primarily as a simple categorical classification task. This paradigm provides limited interpretability of predictions, while leaving the inherent expressiveness and reasoning capabilities of SpeechLLMs largely underutilized. In this work, we take the first step toward reframing AD detection as a deep reasoning task via Reinforcement Learning (RL). We propose ADReasoner, an RL-enhanced SpeechLLM designed to generate accurate diagnostic predictions with interpretable rationales grounded in fine-grained acoustic-linguistic cues. To achieve this, we first systematically construct ADCoT-7K, an AD speech reasoning dataset combining real data and our quality-filtered synthetic data, where each instance features Chain-of-Thought (CoT) annotations and detailed captions. Second, observing the weak perception of AD-related acoustic-linguistic cues in current SpeechLLMs, we leverage Supervised Fine-Tuning (SFT) to develop a cue-perception-enhanced foundation model, ADReasoner-Base, thereby improving AD speech understanding. Third, alongside two standard rule-based rewards, we introduce a reasoning reward and a reasoning-label consistency reward into Group Relative Policy Optimization (GRPO) for RL. Furthermore, a shrinkage mechanism is employed to suppress misleading reasoning signals, while a gating mechanism progressively introduces both rewards to stabilize policy training. Experiments on three AD datasets (Pitt, PROCESS-1, PROCESS-2) demonstrate that ADReasoner outperforms previous state-of-the-art models in both detection performance and explanation quality, advancing AD detection toward interpretable reasoning from speech.